SOTAVerified

Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

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Showing 326–350 of 6661 papers

TitleStatusHype
Decentralized Collective World Model for Emergent Communication and Coordination—0
SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injections—0
SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions—0
Group-based Distinctive Image Captioning with Memory Difference Encoding and Attention—0
All-day Depth Completion via Thermal-LiDAR Fusion—0
ProtoGCD: Unified and Unbiased Prototype Learning for Generalized Category DiscoveryCode1
LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection—0
Overcoming Deceptiveness in Fitness Optimization with Unsupervised Quality-DiversityCode0
Direction-Aware Hybrid Representation Learning for 3D Hand Pose and Shape Estimation—0
Overlap-Aware Feature Learning for Robust Unsupervised Domain Adaptation for 3D Semantic Segmentation—0
All Patches Matter, More Patches Better: Enhance AI-Generated Image Detection via Panoptic Patch Learning—0
SPF-Portrait: Towards Pure Portrait Customization with Semantic Pollution-Free Fine-tuning—0
Cal or No Cal? -- Real-Time Miscalibration Detection of LiDAR and Camera SensorsCode1
CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization—0
Consistent Subject Generation via Contrastive Instantiated Concepts—0
Node Embeddings via Neighbor Embeddings—0
Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering—0
Beyond Contrastive Learning: Synthetic Data Enables List-wise Training with Multiple Levels of RelevanceCode0
CrossMuSim: A Cross-Modal Framework for Music Similarity Retrieval with LLM-Powered Text Description Sourcing and Mining—0
Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach—0
Exploring the Effectiveness of Multi-stage Fine-tuning for Cross-encoder Re-rankersCode0
Fuzzy Cluster-Aware Contrastive Clustering for Time SeriesCode0
Retrieving Time-Series Differences Using Natural Language Queries—0
NeuroLIP: Interpretable and Fair Cross-Modal Alignment of fMRI and Phenotypic Text—0
FakeReasoning: Towards Generalizable Forgery Detection and Reasoning—0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified